Limb Trajectories Decoded from Posterior Parietal Cortex Neural Populations

Abstract

Event Abstract Back to Event Limb Trajectories Decoded from Posterior Parietal Cortex Neural Populations Markus Hauschild1* 1 California Institute of Technology, Division of Biology, United States Abstract: State of the art prosthetic limbs which do not provide sensory feedback force the brain to rely on visual feedback exclusively in order to monitor the artificial limb’s ongoing movement. Primarily visually driven cortical areas such as the Parietal Reach Region (PRR) in Posterior Parietal Cortex (PPC) may therefore be particularly suitable for the extraction of prosthetic limb control signals in absence of useful somatosensory information. We recorded activity from PPC in two monkeys while they performed reaching movements, acquiring randomly placed targets in a 3-dimensional cubic virtual reality workspace. First, PPC population spiking activity recorded during reach trials was examined offline, and later the identified decode model was applied online in a closed-loop-feedback brain-control task where the animals were required to guide a cursor using the neural activity only, without being allowed to move their limbs. We found that PPC provides robust signals allowing the decoding of the current state of ongoing movement (position, velocity and acceleration). Furthermore we were able to demonstrate that a significant degree of spatial learning enabled the animals to cover an increasingly large workspace, whereas temporal learning resulted in the neural substrate to learn and represent previously unknown dynamics such as those of a prosthetic limb. We conclude that PPC is a suitable area for the extraction of prosthetic limb control signals. In the absence of somatosensory feedback, signals from such areas may be more suitable for prosthetic limb control than those obtained from areas with primarily somatosensory inputs. Furthermore, it has been shown that higher level motor variables such as intended reach goals are represented in PPC in addition to directly movement related information. Ongoing research will reveal how such signals can be integrated with decoded trajectories to obtain more accurate trajectory estimates or to allow state machines to transition between a prosthetic limb’s different operating modes, thus further enhancing the functionality of the device. Keywords: computational neuroscience Conference: Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010. Presentation Type: Presentation Topic: Bernstein Conference on Computational Neuroscience Citation: Hauschild M (2010). Limb Trajectories Decoded from Posterior Parietal Cortex Neural Populations. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00042 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Sep 2010; Published Online: 23 Sep 2010. * Correspondence: Dr. Markus Hauschild, California Institute of Technology, Division of Biology, Pasadena, United States, markus@vis.caltech.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Markus Hauschild Google Markus Hauschild Google Scholar Markus Hauschild PubMed Markus Hauschild Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.